Health Information Systems Clinical Decision Support Presentation

Clinical Decision Support (CDS) and Analytics LEARNING HEALTH SYSTEMS Successful CDS Designs • Provide measurable value in addressing a recognized problem area or area for improvement; • Leverage multiple data types to bring the most current and relevant evidence and evidence-based practice recommendations to bear on clinical decisions; • Produce actionable insights from the abundant multiple data sources; • Deliver information to the user that allows the user to make final practice decisions, rather than being opaque and acting autonomously; • Demonstrate good usability principles, including clear displays and rapid action options; • Are testable in small settings with a clear path to larger scalability; and • Support successful participation in quality and value improvement initiatives. CDS Barriers • Lack of reliable, shareable CDS content and capabilities that can be easily adopted across health care organizations and health IT systems; • Absence of systematic means to validate content for provision across delivery venues in a reliable, accessible, and updatable fashion; • The technical difficulties of sharing CDS across institutions and EHR systems; and • Suboptimal user interfaces, implementation choices, and workflows that result in many clinicians viewing CDS more as a nuisance than as a helpful tool. Priorities For Accelerating CDS Progress • Development of CDS content that distills the wealth of information and clinical guidelines into a few action items that will have the biggest impact on patient-centered care. • Learning from CDS implementing experience, including that related to incorporation into the EHR and delivery to the practitioner in a way that provides optimal support for the recommended clinical decisions. • Practical strategies for embedding CDS in real-world environments that considers change management, people management, measurement of use, and usability considerations. • Explication of the value proposition that fosters scale and spread of CDS through the development of clearinghouses and web-based repositories of CDS artifacts that can be shared, evaluated, and continuously improved through feedback from clinicians and patients. CDS Adoption and Use Establish CDS technical standards Publish performance evaluations Engage federal leadership for CDS standards innovation and maturation Promote financing and measurement to accelerate CDS adoption Create a CDS technical information resource Market CDS to stakeholders Disseminate best practices Create a legal framework for CDS Create a national CDS repository Develop a multi-stakeholder CDS learning community to inform usability Measure CDS usage Establish an investment program for CDS research Develop tools to assess CDS efficacy Analytics – Machine Learning Example Outpatient Antibiotic Resistance ◦ Analyze EHR data to predict antibiotic resistance, potentially leading to improved antibiotic stewardship in outpatient settings. ◦ The machine learning algorithm makes a recommendation based on EHR data from more than 10,000 patients ◦ The model predicts the probability that a patient’s Urinary Tract Infection (UTI) can be treated by first- or second-line antibiotics, and then recommend a specific treatment that selects a first-line agent as frequently as possible, without leading to an excess of treatment failures. ◦ The results of the study showed that the algorithm can allow clinicians to reduce use of second-line antibiotics by 67 percent. For patients where providers chose a second-line drug but the algorithm recommended a firstline drug, the first-line drug ended up working more than 90 percent of the time. Find a Healthcare article that references one of the following… Predictive Analytics Machine Learning Artificial Intelligence Big Data Analytics Here are the links to the 3 reading articles: https://www.healthit.gov/sites/default/files/page/2018-04/Optimizing_Strategies_508.pdf https://healthitanalytics.com/news/clinical-decision-support-tool-predicts-resource-utilization https://healthitanalytics.com/news/machine-learning-tool-may-combat-outpatient-antibiotic-resistance …
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